作者: N.M. Duarte , A.E. Ruano , C.M. Fonseca , Bernd Mahn
DOI: 10.1016/S1474-6670(17)36873-8
关键词: Artificial neural network 、 Automatic control 、 Radial basis function 、 Artificial intelligence 、 Genetic algorithm 、 Mathematical optimization 、 Control system design 、 Machine learning 、 Computer science 、 Control system 、 Population
摘要: Designing control systems using multiobjective genetic algorithms can lead to a substantial computational load as result of the repeated evaluation multiple objectives and population-based nature search. A neural network approach, based on radial basis functions, is introduced alleviate this problem by providing computationally inexpensive estimates objective values during straightforward example demonstrates utility approach.